Global analysis of gene expression in mineralizing fish vertebra-derived cell lines: new insights into anti-mineralogenic effect of vanadate

R E S E A R C H A R T I C L E

Open Access

Global analysis of gene expression in mineralizing

fish vertebra-derived cell lines: new insights into

anti-mineralogenic effect of vanadate

Daniel M Tiago

, Vincent Laizé

, Luca Bargelloni

, Serena Ferraresso

, Chiara Romualdi

and M Leonor Cancela

Abstract

Background: Fish has been deemed suitable to study the complex mechanisms of vertebrate skeletogenesis and

gilthead seabream (Sparus aurata), a marine teleost with acellular bone, has been successfully used in recent years

to study the function and regulation of bone and cartilage related genes during development and in adult

animals. Tools recently developed for gilthead seabream, e.g. mineralogenic cell lines and a 4 × 44K Agilent

oligo-array, were used to identify molecular determinants of in vitro mineralization and genes involved in

anti-mineralogenic action of vanadate.

Results: Global analysis of gene expression identified 4,223 and 4,147 genes differentially expressed (fold change

-FC > 1.5) during in vitro mineralization of VSa13 (pre-chondrocyte) and VSa16 (pre-osteoblast) cells, respectively.

Comparative analysis indicated that nearly 45% of these genes are common to both cell lines and gene ontology

(GO) classification is also similar for both cell types. Up-regulated genes (FC > 10) were mainly associated with

transport, matrix/membrane, metabolism and signaling, while down-regulated genes were mainly associated with

metabolism, calcium binding, transport and signaling. Analysis of gene expression in proliferative and mineralizing

cells exposed to vanadate revealed 1,779 and 1,136 differentially expressed genes, respectively. Of these genes, 67

exhibited reverse patterns of expression upon vanadate treatment during proliferation or mineralization.

Conclusions: Comparative analysis of expression data from fish and data available in the literature for mammalian

cell systems (bone-derived cells undergoing differentiation) indicate that the same type of genes, and in some

cases the same orthologs, are involved in mechanisms of in vitro mineralization, suggesting their conservation

throughout vertebrate evolution and across cell types. Array technology also allowed identification of genes

differentially expressed upon exposure of fish cell lines to vanadate and likely involved in its anti-mineralogenic

activity. Many were found to be unknown or they were never associated to bone homeostasis previously, thus

providing a set of potential candidates whose study will likely bring insights into the complex mechanisms of

tissue mineralization and bone formation.

Background

Vertebrate skeleton is a multifunctional organ providing

protection for soft tissues, structural support for muscle

and connective tissues, storage for calcium and

phos-phorus, and a site for haematopoietic cell production

and B lymphocyte maturation in adults [1].

Skeletogen-esis, i.e. skeleton formation during development, is a

process involving complex cellular and molecular

mechanisms associated with ossification and bone

remodeling. Both processes are known to be responsible

for maintaining bone mass and skeletal integrity

throughout life [2]. Skeletogenesis requires concerted

interplay between various cellular activities (e.g.

osteo-blast and chondrocyte differentiation [1]) and numerous

molecular determinants (e.g. skeletal proteins, growth

factors, transcriptional regulators and signaling pathways

[2]). Although human and mouse genetics have greatly

contributed to unveil the mechanisms involved in

skele-togenesis, information remains often insufficient for the

successful development of therapies targeting skeletal

diseases. Recent studies, reviewed by McGonnell and

1_{Centre of Marine Sciences (CCMAR), University of Algarve, Faro, Portugal}

Full list of author information is available at the end of the article

© 2011 Tiago et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fowkes [3], have demonstrated the suitability of fish

models to investigate vertebrate development and in

particular skeletogenesis. The resemblance of

biochem-ical and physiologbiochem-ical processes from fish to mammals,

the presence in fish of orthologs for most mammalian

genes, the similarities in organ morphology and systems

composition are among the traits that contributed to

the recent and rapid interest in fish models. Those

com-bined with technical advantages, e.g. large progeny,

external reproduction fast growth and translucent larvae,

and the power of fish genetics, have definitively

trans-formed fish systems as promising alternatives to

mam-malian systems. In fact, various fish mutants have

already been used to model human skeletal diseases:

osteogenesis imperfecta (an autosomal dominant

disor-der characterized by extreme bone fragility) can be

modeled by zebrafish chihuahua mutant [4]; craniofacial

syndromes (holoprosencephaly, campomelic dysplasia

and Ehlers-Danlos syndrome) can be modeled by

zebra-fish sonic you, jellyzebra-fish, and b4galt7 mutants [5,6],

idio-pathic scoliosis can be modeled by guppy curve back

mutant [7] and important efforts have consequently

been made towards the development of fish biochemical,

molecular, and cellular tools [8]. These include: (i) the

sequence of genomes of various fish models, e.g.

zebra-fish, green-spotted puffer zebra-fish, Japanese medaka and

stickleback; (ii) the development of large collections of

expressed sequence tags (EST) which were produced for

several fish species (e.g. Atlantic salmon [9], rainbow

trout [10], Atlantic halibut [11] or channel and blue

cat-fish [12]); (iii) the development of several microarray

platforms to explore these EST collections (e.g.

Nimble-gen Technology high-density oligo-array for the catfish

[13], parallel synthesis technology high-density DNA

microarray for the Atlantic halibut [14], Agilent

Sure-Print

™ Technology oligo-array for the largemouth bass

[15], the rainbow trout [16], and the gilthead seabream

Sparus aurata

[17]); and (iv) the development of several

fish-derived cell lines (the Fish Cell Line Database,

http://www.fcma.ualg.pt/edge/FICELdb.mht). To

investi-gate mechanisms of tissue mineralization, various

bone-derived cell lines of fish origin are available [18] and

gilthead seabream VSa13 and VSa16 cell lines (derived

from vertebra) are of particular interest due to their

pre-chondrocyte and pre-osteoblast phenotypes.

Although they are both capable of mineralizing their

extracellular matrix, VSa13 and VSa16 cell lines behave

differently regarding their degree of mineral deposition

[19], levels of alkaline phosphatase activity [19],

expres-sion of mineralogenic genes, e.g. matrix gla protein

(MGP), osteocalcin (OC), osteopontin (SPP1), bone

mor-phogenetic protein-2 (BMP-2)

[19-21], and susceptibility

to mineralogenic or anti-mineralogenic molecules,

e.g.

insulin, IGF-1, vanadate [22,23] and retinoic acid

[unpublished results]. They are therefore considered as

different bone cell types and common genes

differen-tially expressed during in vitro mineralization should

represent key mineralogenic genes, while other

differen-tially expressed genes would represent genes involved in

cell type-specific processes not related to mineralization.

In this work, we have used an Agilent Sureprint 4 ×

44K oligo-array, containing two non-overlapping probes

for each of 19,734 unique gene transcripts [17], to

ana-lyze global gene expression during in vitro

minerali-zation of these two cell lines. We have also identified

in VSa13 cell line the presence of mineralogenic

genes whose expression was altered upon cell

expo-sure to vanadate, a molecule with anti-mineralogenic

activity [23].

Results

Genes differentially expressed during

in vitro

mineralization of gilthead seabream vertebra-derived cell

lines

Confluent cultures of VSa13 and VSa16 cells were

culti-vated during 4 weeks under control (regular medium)

or mineralizing (regular medium supplemented with

cal-cium, phosphate and L-ascorbic acid) conditions.

Deposition of mineral nodules within extracellular

matrix was confirmed by von Kossa staining in cells

exposed to mineralization cocktail (Figure 1) and total

RNA was extracted from three biological replicates per

condition. After proper amplification and labeling, each

RNA sample was hybridized against the oligo-array and

raw expression data were extracted and filtered using

Figure 1 Pictures of chondrocyte (VSa13) and pre-osteoblast (VSa16) cells undergoing mineralization. Cultures were treated for 4 weeks under control or mineralizing conditions then von Kossa stained to reveal mineral deposition.

Agilent Feature Extraction 9.5.1 software, then

normal-ized. Quantile normalization showed the highest

agree-ment among replicates (i.e. lowest variation of

normalized fluorescence distribution among replicates;

data not shown) and was consequently used to

normal-ize raw data sets. Normalnormal-ized data sets were analyzed

through significance analysis of microarray (SAM) and

genes differentially expressed in control versus

minera-lizing conditions were identified. False discovery rate

(FDR) threshold was set at 5% and only probes with fold

change (FC) over 1.5 were considered. A total of 4,777

and 4,554 probes - corresponding to 3,011 and 3,049

unique genes - indicative of an up-regulated expression

were identified from VSa13 and VSa16 RNAs,

respec-tively. Among these genes, 1,489 were shown to be

common to both cell lines (Figure 2). Similarly, 2,359

and 1,642 probes - corresponding to 1,212 and 1,098

unique genes - indicative of a down-regulated

expres-sion were identified from VSa13 and VSa16 RNAs,

respectively. Among these genes, 469 were shown to be

common to both cell lines (Figure 2). In VSa13 and

VSa16 cells, 69% and 49% of differentially expressed

genes were simultaneously detected by two

non-overlap-ping probes, respectively, which could indicate the high

occurrence of alternative splicing. It could also be the

consequence of slight differences of hybridization

between the two probes. Indeed, most genes detected by

only one probe seem to follow the same pattern of

expression when compared to the second

non-signifi-cant probe. Raw and normalized fluorescence data have

been deposited in the GEO database under accession

numbers GSE18915 (VSa13) and GSE18941 (VSa16).

Ontology of mineralization-related genes

Genes differentially expressed during in vitro

mineraliza-tion were classified according to their putative Gene

Ontology (GO) - biological process (BP), molecular

function (MF) and cellular component (CP) - using

OBO-Edit software, AmiGO database [24] and SAPD

database [25], based on significant similarity with known

genes in public data bases. In general, a GO term could

be associated with less than 25% of

mineralization-related genes (% of BP/MF/CP was 19.2/24.3/18.5 in

VSa13 cells and 16.6/20.7/10.3 in VSa16 cells).

Com-parative analysis of GO classifications indicated that the

same type of genes, but not necessary the same genes,

were involved in extracellular matrix (ECM)

mineraliza-tion of both cell lines (Figure 3). Moreover, similar

occurrence of BP, MF and CP classes was observed in

genes common to both cell lines (Additional files 1, 2

and 3, Tables S1, S2 and S3). The most represented BP

classes (Figure 3A and Additional file 1, Table S1) were:

(i) metabolism (52.9% and 55.9% in VSa13 and VSa16

cells, respectively), (ii) establishment of localization (16%

and 16.1%; mostly related to transport), (iii) cellular

pro-cesses (14.4% and 12.4%, mostly related to signaling)

and (iv) regulation (7.8% and 6.9%, mostly associated to

cell cycle). Most MF classes were related to binding

activity (47.2% and 45.4%; mostly to nucleotides, ions

and nucleic acid) and catalytic activity (36.5% and

38.4%) (Figure 3B and Additional file 2, Table S2). A

smaller part was involved in transport, enzyme

regula-tion and molecular transducregula-tion activities (total of

10.1% and 10.7% in VSa13 and VSa16 cells,

respec-tively). Finally, most CP classes were related to cytosol

or membrane compartments (61.1% and 59.0%) and to

specific organelles (14.6% and 17.6%; nucleus,

endoplas-mic reticulum or Golgi complex). A smaller fraction was

associated to macromolecular complexes (9.9% and

8.0%), extracellular region (6.6% and 8.0%) and organelle

parts (6.7% and 6.4%). In a general manner, the

identifi-cation of numerous and diverse genes in both cell lines

suggested that ECM mineralization is a complex process

that requires tight regulatory mechanisms. In order to

understand which type of differentially expressed genes

were enriched in these cell lines, we performed a

func-tional annotation analysis using the

Database for

Anno-tation,

Visualization and Integrated Discovery (DAVID

v6.7, available at http://david.abcc.ncifcrf.gov[26,27]) and

VSa13

VSa16

1522

1489

1560

743

469

629

A

B

VSa13

VSa16

Figure 2 Venn diagrams of genes up-regulated (A) and down-regulated (B) during mineralization of VSa13 and VSa16 cells. A two class SAM test was performed; FDR and FC parameters were lower than 5% and higher than 1.5, respectively. Size of diagrams is proportional to the size of gene pools.

focused on processes with fold enrichment > 1.1 and

significance p-value < 0.05. In both cell lines, results

indicated an enrichment of genes associated with

bio-synthetic processes, cell cycle and growth, signaling,

protein synthesis, stress response, biological regulation

and metabolism (Table 1). Other processes such as

pro-tein interaction, DNA/RNA metabolism, development,

adhesion and bone, were cell type-specific (only

identi-fied in VSa16 cells), confirming that although both cell

lines are mineralogenic, they also represent different

bone cell types.

In an attempt to pinpoint the most relevant

mineralo-genic genes, we looked at genes with a FC > 10 (FDR

was maintained to < 5%). A total of 46/48 up-regulated

and 49/49 down-regulated genes were identified in

VSa13 and VSa16 cells, respectively. Most of them (61%

in VSa13 cells and 59% in VSa16 cells) did not match

any known gene (Additional files 4, 5, 6 and 7, Tables

S4, S5, S6 and S7). Of these genes, 33 were common to

both cell lines, GO analysis indicated that up-regulated

genes were mainly associated with (i) transport, (ii)

matrix/membrane, (iii) metabolism and (iv) signaling,

while down-regulated genes were mainly associated with

(i) metabolism, (ii) calcium binding, (iii) transport and

(iv) signaling.

Proliferative and anti-mineralogenic effects of vanadate:

identification of genes with significant patterns of

expression

In order to further investigate genes involved in ECM

mineralization, we exposed dividing and mineralizing

VSa13 cells to vanadate, an ultra-trace metal with

anti-mineralogenic effect in fish vertebra-derived cell lines,

and analyzed global gene expression. Any gene

differen-tially expressed upon vanadate treatments would be

potentially important for differentiation/mineralization

mechanisms. As expected from previous studies [23,28],

cell proliferation was stimulated by vanadate at

concen-trations up to 7.5

μM, while ECM mineralization was

inhibited by vanadate at concentrations up to 5

μM

(Figure 4). Total RNA was collected from three

biologi-cal replicates of proliferating and mineralizing VSa13

cells exposed to vanadate or left untreated. After proper

amplification and labeling, each RNA sample was

hybri-dized against gilthead seabream oligo-array and data

were extracted, normalized, and analyzed as previously

described. Differentially expressed genes were identified

through a two-class SAM analysis with FDR < 5% and

FC > 1.5 in the following data sets: i) control versus

mineralization: 4,223 differentially expressed genes

(3,011 up- and 1,212 down-regulated genes), ii)

minera-lization versus mineraminera-lization + vanadate: 1,136

differen-tially expressed genes (406 up- and 730 down-regulated

genes), and iii) proliferation versus proliferation +

vana-date: 1,779 differentially expressed genes (496 up- and

1283 down-regulated genes). Among those genes, 342

were common to conditions i) and ii) (Figure 5A). In

order to identify key mineralogenic genes in VSa13 cells,

we looked at genes which expression was oppositely

regulated during in vitro mineralization and upon

vana-date treatment. These genes and their respective GO

categories are listed in Table 2 and could be classified

according to a score calculated as the ratio between

FCM

(control versus mineralization) and FCMV

(minera-lization versus minera(minera-lization + vanadate). Most genes

among genes detected in VSa13 and VSa16 cells. Pie charts represent biological processes, molecular function and cellular component GO entries occurrence among differentially expressed genes in control versus mineralized VSa13 and VSa16 cells. Raw data were normalized using the quantile method and a two class SAM test was performed; FDR and FC were lower than 5% and higher than 1.5, respectively. Biological processes GO entries were associated with 19.2% (VSa13) and 16.6% (VSa16) of identified genes; molecular function GO entries were associated with 24.3% (VSa13) and 20.7% (VSa16) of identified genes; and cellular component GO entries were associated to 18.5% (VSa13) and 10.3% (VSa16) of identified genes. GO definitions: BA, biological adhesion; ER, extracellular region; ERA, enzyme regulator activity; MC, macromolecular complex; MCSO, macromolecular complex subunit organization; MTA, molecular transducer activity; OP, organelle part; RS, response to stimulus; SMA, structural molecule; TA, transporter activity. Additional information on GO definition is available in Additional files 1, 2 and 3, Tables S1, S2 and S3.

Table 1 List of biological processes GO categories enriched among genes differentially expressed in mineralizing

VSa13 and VSa16 cells

VSa13 (CHONDROCYTIC LINEAGE) VSa16 (OSTEOBLASTIC LINEAGE)

BP TERM COUNT % P FE TERM COUNT % P FE

protein homotetramerization 6 0.5 0.006 3.8 PROT. INTER. protein tetramerization 7 0.5 0.013 2.9 protein homooligomerization 9 0.7 0.024 2.3 rRNA transcription 5 0.4 0.019 3.8 DNA/RNA METAB. DNA replication 37 2.9 0.000 2.1 DNA-dependent DNA replication 19 1.5 0.001 2.1 serine family amino acid biosynt. process 6 0.4 0.025 2.9 vitamin biosynthetic process 10 0.8 0.003 2.7 hexose/alcohol/monosaccharide biosynthetic

process

12 0.8 0.005 2.3 water-soluble vitamin biosynthetic process

9 0.7 0.008 2.6 BIOSYN.

PROC.

amino acid biosynthetic process 15 1.0 0.043 1.7 hexose/alcohol/monosaccharide biosynthetic process

10 0.8 0.028 2.1 carbohydrate biosynthetic process 21 1.5 0.016 1.6 coenzyme biosynthetic process 21 1.6 0.012 1.7 nitrogen compound biosynthetic process 21 1.5 0.027 1.6 cofactor biosynthetic process 24 1.9 0.035 1.5

amine biosynthetic process 19 1.3 0.040 1.5

pigmentation 7 0.5 0.026 2.6 inner ear morphogenesis 7 0.5 0.045 2.4 fertilization 9 0.7 0.024 2.3

DEV. tissue remodeling 14 1.1 0.009 2.0

embryonic morphogenesis 14 1.1 0.013 2.0 tissue development 33 2.5 0.005 1.6 embryonic development 33 2.5 0.010 1.5 mitochondrion organization and biogenesis 15 1.0 0.016 1.8 mitotic cell cycle checkpoint 9 0.7 0.024 2.3 cell motility/localization of the cell 59 4.1 0.002 1.4 cell cycle checkpoint 12 0.9 0.015 2.1 mitosis/M phase of mitotic cell cycle 35 2.4 0.024 1.4 regulation of mitosis 14 1.1 0.009 2.0 cell projection: morphogenesis, 28 2.0 0.040 1.4 growth 38 2.9 0.001 1.6 CELL

CYCLE

organization and biogenesis regulation of cell growth 18 1.4 0.042 1.6 AND M phase 40 2.8 0.034 1.3 regulation of growth 25 1.9 0.018 1.5 GROWTH cell division 36 2.5 0.041 1.3 cell growth 24 1.9 0.023 1.5 cell proliferation 89 6.2 0.013 1.2 regulation of cell size 24 1.9 0.035 1.5 cell motility/localization of the cell 52 4.0 0.007 1.4

mitosis/M phase of mitotic cell cycle

33 2.5 0.018 1.4 cell migration 31 2.4 0.027 1.4 mitotic cell cycle 43 3.3 0.039 1.3 cell proliferation 78 6.0 0.047 1.2 SIGNAL. DNA damage response. signal transduction

resulting in induction of apoptosis

5 0.3 0.028 3.4 phosphoinositide-mediated signaling

16 1.2 0.010 1.9 PROT. tRNA modification 6 0.4 0.010 3.4 tRNA aminoacylation for protein

translation/aminoacid activation

14 1.1 0.025 1.8 SYNT. RNA modification 9 0.6 0.001 3.1

response to virus 14 1.0 0.003 2.2 response to wounding 50 3.9 0.006 1.4 STRESS cell redox homeostasis 17 1.2 0.003 2.0 inflammatory response 32 2.5 0.049 1.4 RESP. response to oxidative stress 19 1.3 0.040 1.5 response to stress 112 8.6 0.028 1.2 response to stimulus 216 15.1 0.011 1.1 response to stimulus 194 15.0 0.026 1.1

ADH. biological/cell adhesion 65 5.0 0.026 1.3

BIOL. REG. regulation of biological quality 103 7.2 0.018 1.2 regulation of biological quality 97 7.5 0.008 1.2 isoprenoid metabolic process 9 0.6 0.015 2.4 purine ribonucleoside metabolic

process

Table 1 List of biological processes GO categories enriched among genes differentially expressed in mineralizing

VSa13 and VSa16 cells (Continued)

glutathione metabolic process 8 0.6 0.033 2.3 ribonucleoside/glycine metabolic process

6 0.5 0.017 3.2 pyruvate metabolic process 11 0.8 0.017 2.1 retinoid/diterpenoid metabolic

process

5 0.4 0.046 3.1 gluconeogenesis 9 0.6 0.043 2.1 aldehyde metabolic process 6 0.5 0.035 2.8 mitochondrial transport 12 0.8 0.032 1.9 serine family amino acid metabolic

process

12 0.9 0.002 2.5 sulfur metabolic process 17 1.2 0.010 1.8 glutamine family amino acid metab.

process

12 0.9 0.004 2.4 water-soluble vitamin metabolic process 13 0.9 0.049 1.7 nucleoside/pyridine nucleotide

metab. process

10 0.8 0.011 2.4 glucose metabolic process 26 1.8 0.010 1.6 water-soluble vitamin metabolic

process

16 1.2 0.001 2.3 glucose catabolic process 17 1.2 0.037 1.6 gluconeogenesis 9 0.7 0.024 2.3 vitamin/alcohol metabolic process 18 1.3 0.039 1.6 oxygen and ROS metabolic process 8 0.6 0.033 2.3 nucleotide metabolic process 47 3.3 0.001 1.5 vitamin metabolic process 23 1.8 0.000 2.2 nucleobase. nucleoside and nucleotide

metabolic process

49 3.4 0.002 1.5 pyruvate metabolic process 10 0.8 0.028 2.1 monosaccharide metabolic process 32 2.2 0.016 1.5 glucose metabolic process 26 2.0 0.002 1.8 carboxylic/organic acid metab. process 102 7.1 0.000 1.4 glucose catabolic process 17 1.3 0.015 1.8 amino acid and derivative metabolic process 67 4.7 0.002 1.4 amino acid catabolic process 15 1.2 0.025 1.8 amino acid metabolic process 54 3.8 0.008 1.4 alcohol catabolic process 18 1.4 0.015 1.7 monocarboxylic acid metabolic process 45 3.1 0.015 1.4 hexose/monosaccharide catabolic

process

17 1.3 0.026 1.7 hexose metabolic process 31 2.2 0.023 1.4 amine/nitrogen compound

catabolic process

16 1.2 0.034 1.7 fatty acid metabolic process 31 2.2 0.034 1.4 monosaccharide metabolic process 31 2.4 0.007 1.6 METAB. lipid biosynthetic process 32 2.2 0.040 1.4 aromatic compound metabolic

process

23 1.8 0.012 1.6 amine metabolic process 74 5.2 0.006 1.3 carbohydrate catabolic process 21 1.6 0.025 1.6 nitrogen compound metabolic process 75 5.2 0.010 1.3 tRNA metabolic process 19 1.5 0.026 1.6

alcohol metabolic process 47 3.3 0.021 1.3 cellular carbohydrate catabolic process

20 1.5 0.032 1.6 cellular carbohydrate metabolic process 52 3.6 0.036 1.3 amino acid metabolic process 54 4.2 0.001 1.5

catabolic process 106 7.4 0.012 1.2 nucleobase. nucleoside and nucleotide metabolic process

46 3.5 0.001 1.5 lipid metabolic process 85 5.9 0.012 1.2 nucleotide metabolic process 42 3.2 0.003 1.5 cellular lipid metabolic process 69 4.8 0.037 1.2 coenzyme metabolic process 37 2.9 0.011 1.5 hexose metabolic process 30 2.3 0.011 1.5 carboxylic/organic acid metabolic

process

97 7.5 0.000 1.4 amine metabolic process 72 5.6 0.001 1.4 amino acid and derivative

metabolic process

62 4.8 0.002 1.4 nitrogen compound metabolic

process

73 5.6 0.002 1.4 cofactor metabolic process 46 3.5 0.007 1.4 cellular carbohydrate metabolic

process

51 3.9 0.009 1.4 carbohydrate metabolic process 67 5.2 0.009 1.3 alcohol metabolic process 43 3.3 0.026 1.3 cellular catabolic process 84 6.5 0.017 1.2 catabolic process 95 7.3 0.027 1.2 lipid metabolic process 75 5.8 0.037 1.2 cellular lipid metabolic process 63 4.9 0.043 1.2

were shown to be associated with metabolism, cell

matrix/adhesion, signaling and calcium binding. Fewer

genes were associated with apoptosis, transport,

proteo-lysis, structural activity, translation and growth.

Finally, proliferative and anti-mineralogenic effects of

vanadate suggest that its mechanism of action may be

associated with cell differentiation. Therefore, genes

that followed i) opposite expression between

minerali-zation and mineraliminerali-zation upon vanadate treatment and

ii) concordant expression between vanadate treatments

during proliferation and vanadate treatments during

mineralization should be of particular interest. A total

of 136 genes were differentially expressed when

com-paring data sets of dividing and mineralizing cell

cul-tures exposed to vanadate. Among those genes, 87

genes were also differentially expressed during in vitro

mineralization without vanadate (Figure 5B) and genes

that fit into the pattern of expression described above

were associated to their respective GO categories and

listed in Table 3 according to highest FC values. Most

genes were associated with metabolism, signaling

and cell matrix adhesion. Fewer genes were related to

proteolysis, calcium binding, cell cycle and DNA

replication.

Validation of microarray data by quantitative real-time

PCR analysis of gene expression

Twelve genes differentially expressed during in vitro

mineralization as for microarray data (6 up- and 6

down-regulated genes) were selected for validation by

quantitative real-time PCR (qPCR) according to the

fol-lowing criteria: two genes with FC > 10, two genes with

10 > FC > 2, and two genes with 2 > FC > 1.5 for each

cell line. Comparative analysis of microarray and qPCR

expression data is presented in Figure 6. Analysis of

Pearson correlation revealed coefficients higher than 0.9

for both cell lines (strong correlation) when comparing

data from microarray probes 1 and 2, and coefficients

between 0.4 and 0.5 for VSa13 cells (moderate

correla-tion) and between 0.5 and 0.8 for VSa16 cells

(moder-ately high correlation) when comparing data from

microarray and qPCR. The individual analysis of each

gene revealed that FC measured by qPCR were always

higher (p < 0.05) than that measured by microarray

(with the exception of RAR-b in VSa13 cells), suggesting

a higher sensitivity of qPCR analysis.

Similarly, 6 genes differentially expressed in

mineraliz-ing cells exposed to vanadate (3 up- and 3

down-regu-lated genes) were selected for validation of microarray

Table 1 List of biological processes GO categories enriched among genes differentially expressed in mineralizing

VSa13 and VSa16 cells (Continued)

bone mineralization 6 0.5 0.035 2.8

BONE ossification/biomineral formation/

bone remodelling

13 1.0 0.011 2.0 skeletal development 28 2.2 0.009 1.6

Functional annotation tool from the database for annotation, visualization and integrated discovery (DAVID; v6.7) has been used to identify significant biological processes among differentially expressed genes. p < 0.05 (P) and fold enrichment (FE) was higher than 1.1.

Figure 4 Effect of vanadate on VSa13 cell proliferation (A) and ECM mineralization (B). For proliferation experiments VSa13 cells were seeded in 96-well plates at 1.5 × 103cells/well then either left untreated or treated with 2.5, 5 and 7.5μM of vanadate. Cell proliferation was evaluated after 8 days using MTS assay. For ECM mineralization experiments VSa13 cells were seeded in 24-well plates at 2 × 104cells/well, grown until confluence then treated for mineralization. Mineralizing cultures were then either left untreated or treated with 5μM vanadate. Mineral deposition was revealed after 4 weeks by von Kossa staining and evaluated by densitometry analysis. Asterisk indicates values statistically different from corresponding controls (n_{≥ 3; P < 0.05; one-way ANOVA).}

data by qPCR (Figure 7). Analysis of Pearson

correla-tion revealed coefficients higher than 0.99 (strong

cor-relation) when comparing data from microarray probes

1 and 2, and higher than 0.9 (strong correlation) when

comparing data from microarray and qPCR. In general,

correlation coefficients were shown to be higher

among genes analyzed in vanadate-treated VSa13

cells than among those analyzed in mineralizing VSa13

and VSa16 cells. This difference could in part be

explained by the lower FC values observed among

genes identified in vanadate-treated cells and

conse-quent lower tendency for FC compression of

oligo-array data.

Discussion

Gilthead seabream oligo-array is a suitable tool to

analyze global gene expression of mineralogenic gilthead

seabream cell lines

In the present study, a comprehensive set of data has

been produced following hybridization of an Agilent

4x44K oligo-array with RNA samples prepared from

control, mineralizing and vanadate-treated gilthead

seab-ream vertebra-derived cell samples. A two-class SAM

analysis identified mineralogenic genes, some of which

had already been previously investigated during in vitro

mineralization of VSa13 and VSa16 cells through a

can-didate gene approach, e.g. tissue non-specific alkaline

phosphatase

(TNAP; unpublished data), BMP-2 [21],

SPP1

[20], MGP and OC [19]. At first glance, a good

correlation (i.e. similar type and extent of regulation)

between microarray and pre-existing data was observed.

This good correlation was later confirmed through

qPCR analysis, although a tendency for FC value

com-pression of oligo-array data was also noted. A similar

fold-change compression was observed by Wang et al.

while validating two commercial long-oligonucleotide

microarray platforms, Applied Biosystems and Agilent,

through large scale qPCR analysis of gene expression

[29], and more recently by Ferraresso and colleagues

while using Agilent gilthead seabream oligo-array [17].

Although our microarray data exhibited a dynamic

range inferior to that of qPCR data, a situation which

was somehow expected, they were generally accurate;

Agilent seabream oligo-array therefore represent a

valu-able tool for simultaneous analysis of the expression of

thousands of genes.

In vitro mineralization recruits similar genes in fish and in

mammalian bone-derived systems

Putative mineralogenic genes, i.e. those differentially

expressed during in vitro mineralization of VSa13 and

VSa16 cells, presented a similar distribution into GO

categories. Approximately half of those genes were

shown to be common to VSa13 and VSa16 cells,

indi-cating that both cell lines, although representing

differ-ent cell types, recruit similar genes and processes

during mineralization. Some biological processes were

however differentially enriched in both cell lines. In

particular, genes related to GO categories bone

minera-lization, biomineral formation, remodeling, ossification

and skeletal development were enriched in mineralizing

VSa16 pre-osteoblast cells, and not in mineralizing

VSa13 pre-chondrocyte cells. Another goal of this

study was to investigate the conservation of

mineraliza-tion mechanisms throughout evolumineraliza-tion by comparing

pattern of gene expression in fish and mammalian

C/M

4223

M/MV

1136

P/PV

1779

C/M

4223

M/MV

1136

A

B

342

87

Figure 5 Venn diagrams of differentially expressed genes in vanadate-treated VSa13 cells. (A) Genes detected in mineralizing cells (C/M) were compared with genes detected in mineralizing cells treated with vanadate (M/MV). (B) Genes detected in C/M were compared with genes detected in M/MV and with genes detected in proliferating cells treated with vanadate (P/PV). Two-class SAM tests were performed with FDR and FC limits lower than 5% and higher than 1.5, respectively. Size of diagrams is proportional to the size of gene pools.

Table 2 Gene description (according to SAPD database [28]), GO classification, FC and scores of common genes

regulated during mineralization of VSa13 cells (FC

)

versus mineralization with vanadate (FC

)

Gene description GOBP/MF FCM FCMV Score

Photoreceptor outer segment all-trans retinol dehydrogenase [Source:IPIAcc:IPI00024598]

metabolic process/oxidoreductase activity 34,8 0,14 240,1 AMBP protein precursor [Source:Uniprot/SWISSPROTAcc:P02760] -/- 47,4 0,36 131,5 Ependymin related protein-1 precursor [Source:IPIAcc:IPI00554718] cell-matrix adhesion/calcium ion binding 15,2 0,14 111,8 Xaa-Pro aminopeptidase 2 precursor [Source:Uniprot/

SWISSPROTAcc:O43895]

proteolysis, creatine metabolic process/ metalloexopeptidase activity, hydrolase activity, creatinase

activity

27,2 0,25 110,2

No match -/- 28,2 0,31 89,73

Microtubule-associated proteins 1A/1B light chain 3C precursor [Source:Uniprot/SWISSPROTAcc:Q9BXW4]

-/- 12,6 0,14 89,63

Cell death activator CIDE-3 [Source:Uniprot/SWISSPROTAcc: Q96AQ7]

apoptosis/protein binding 7,77 0,17 45,71

No match -/- 25,5 0,61 41,50

No match -/- 12,7 0,32 39,97

PREDICTED: hypothetical protein [Danio rerio] pore complex biogenesis/channel activity 18,3 0,52 35,20 Hypothetical protein LOC447879 [Source:RefSeq_peptideAcc:

NP_001004618]

integrin-mediated signaling pathway/- 9,55 0,29 32,64

No match -/- 17,5 0,54 32,31

Granulocyte-macrophage colony-stimulating factor receptor alpha chain precursor [Source:Uniprot/SWISSPROTAcc:P15509]

-/- 8,69 0,29 29,83

No match -/- 5,77 0,21 27,18

IgGFc-binding protein precursor [Source:Uniprot/SWISSPROTAcc: Q9Y6R7]

cell adhesion/- 4,35 0,19 23,12

No match -/- 6,28 0,28 22,13

Hydroxyacid oxidase 2 [Source:Uniprot/SWISSPROTAcc:Q9NYQ3] metabolic process, electron transport/oxidoreductase activity

5,21 0,24 21,38

No match -/- 7,70 0,39 19,85

No match -/- 7,35 0,37 19,79

Phosphatase and actin regulator 4 isoform 1 [Source: RefSeq_peptideAcc:NP_001041648]

-/- 4,95 0,27 18,08

Ependymin related protein-1 precursor [Source:IPIAcc:IPI00554718] cell-matrix adhesion/calcium ion binding 2,54 0,14 18,02

No match -/- 8,95 0,52 17,26

No match -/- 10,6 0,62 17,00

No match -/- 6,53 0,39 16,57

Glucose-6-phosphate 1-dehydrogenase [Source:Uniprot/ SWISSPROTAcc:P11413]

glucose metabolic process/glucose-6-phosphate dehydrogenase activity

4,30 0,27 16,05 Glutamate–cysteine ligase catalytic subunit [Source:Uniprot/

SWISSPROTAcc:P48506]

-/- 4,71 0,29 16,04

Complement factor I precursor [Source:Uniprot/SWISSPROTAcc: P05156]

proteolysis/catalytic activity, serine-type endopeptidase activity, hydrolase activity, scavenger receptor activity

2,58 0,16 15,91 No match -/- 5,66 0,41 13,71 No match -/- 4,30 0,33 12,95 No match -/- 3,64 0,29 12,77 No match -/- 4,93 0,39 12,73 No match -/- 5,41 0,43 12,69

Hypothetical protein LOC406276 [Source:RefSeq_peptideAcc: NP_998168]

Table 2 Gene description (according to SAPD database [28]), GO classification, FC and scores of common genes

regu-lated during mineralization of VSa13 cells (FC

)

versus mineralization with vanadate (FC

) (Continued)

Hypothetical protein LOC336637 [Source:RefSeq_peptideAcc: NP_956317]

cell redox homeostasis/- 4,66 0,38 12,17 Actin filament-associated protein 1-like 2. [Source:Uniprot/

SWISSPROTAcc:Q8N4X5]

-/- 5,69 0,48 11,79

Transcribed locus, weakly similar to NP_175293.1 [Source: UniGeneAcc:Tru.931]

metabolic process/methyltransferase activity 2,82 0,24 11,69

No match -/- 5,96 0,51 11,66

Ornithine carbamoyltransferase, mitochondrial precursor [Source: Uniprot/SWISSPROTAcc:P00480]

-/ornithine carbamoyltransferase activity, amino acid binding

4,17 0,37 11,38

No match -/- 4,83 0,42 11,38

CDNA FLJ31025 fis, clone HLUNG2000501. [Source:Uniprot/ SPTREMBLAcc:Q96ND9]

-/- 3,80 0,34 11,25

No match -/- 3,71 0,34 11,05

No match -/- 4,39 0,40 10,89

No match -/- 4,76 0,45 10,59

Stromal cell-derived factor 1 precursor [Source:Uniprot/ SWISSPROTAcc:P48061]

-/- 3,94 0,38 10,36

ADM precursor [Source:Uniprot/SWISSPROTAcc:P35318] -/- 3,68 0,36 10,10 Beta-2-glycoprotein 1 precursor [Source:Uniprot/SWISSPROTAcc:

P02749]

-/- 4,44 0,44 10,05

No match -/- 3,37 0,34 10,03

No match -/- 0,05 5,03 102,8

Signal peptide, CUB and EGF-like domain-containing protein 2 precursor [Source:Uniprot/SWISSPROTAcc:Q9NQ36]

-/calcium ion binding 0,09 7,02 78,01

No match -/- 0,18 10,7 58,98

Actin, alpha skeletal muscle 1. [Source:Uniprot/SWISSPROTAcc: P68140]

-/structural molecule activity, ATP binding, protein binding 0,44 17,1 38,39

No match -/- 0,05 1,77 36,79

No match -/- 0,09 3,03 34,37

No match -/- 0,45 14,9 33,47

Inter-alpha globulIn InhIbItor H3 [Source:IPIAcc:IPI00028413] hyaluronan metabolic process/serine-type endopeptidase inhibitor activity

0,18 4,41 24,67

No match -/- 0,09 1,92 21,62

Hypothetical protein LOC553753 [Source:RefSeq_peptideAcc: NP_001018560]

-/protein binding 0,13 2,84 21,14

No match -/- 0,15 3,18 20,60

Tenascin precursor [Source:Uniprot/SWISSPROTAcc:P24821] signal transduction/receptor binding 0,19 3,87 20,56

No match -/- 0,14 2,77 20,33

Prolargin precursor [Source:Uniprot/SWISSPROTAcc:P51888] -/- 0,16 3,06 18,89 Eukaryotic translation initiation factor 4E-1A-binding protein

[Source:Uniprot/SWISSPROTAcc:Q98TT6]

negative regulation of translational initiation/eukaryotic initiation factor 4E binding

0,24 2,84 12,02

No match -/- 0,40 4,76 11,87

No match -/- 0,18 2,01 11,27

WNT1-inducible-signaling pathway protein 1 precursor [Source: Uniprot/SWISSPROTAcc:O95388]

regulation of cell growth/insulin-like growth factor binding 0,26 2,78 10,77

No match -/ 0,29 3,00 10,34

Genes were selected according to their patterns of expression (1st

group: FCM> 1.5 and FCMV< 0.67; 2ndgroup: FCM< 0.67 and FCMV> 1.5) and highest scores

(1st

group: FCM/FCMV> 10; 2ndgroup: FCMV/FCM> 10). GO classification was subdivided in biological processes (BP) and molecular function (MF). Two-class SAM

Table 3 Gene description (according to SAPD database [28]), GO classification and FC common genes regulated during

mineralization of VSa13 cells (FC

)

versus mineralization + vanadate (FC

)

versus proliferation + vanadate

Gene description GOBP/MF FCM FCMV FCPV

Ependymin related protein-1 precursor [Source:IPIAcc:IPI00554718] cell-matrix adhesion/Ca ion binding 15.21 0.14 0.59

No match -/- 5.77 0.21 0.28

Xaa-Pro aminopeptidase 2 precursor [Source:Uniprot/ SWISSPROTAcc:O43895]

creatine metabolic process, proteolysis/metalloexopeptidase activity, hydrolase activity, creatinase activity

27.2 0.25 0.35

No match -/- 2.13 0.30 0.24

No match -/- 3.37 0.34 0.54

Beta-2-glycoprotein 1 precursor [Source:Uniprot/SWISSPROTAcc: P02749]

-/- 4.44 0.44 0.25

Translation initiation factor eIF-2B subunit [Source:Uniprot/ SWISSPROTAcc:Q9UI10]

-/- 1.96 0.49 0.58

Rap1b. [Source:Uniprot/SPTREMBLAcc:Q9YH37] small GTPase mediated signal transduction/GTP binding 2.08 0.51 0.51 Ribonucleoside-diphosphate reductase large subunit [Source:

Uniprot/SWISSPROTAcc:P23921]

DNA replication/ribonucleoside-diphosphate reductase activity, protein binding

2.19 0.51 0.66 C9orf119 [Source:Uniprot/SPTREMBLAcc:Q8N2W6] -/- 2.65 0.53 0.59

No match -/- 4.00 0.53 0.43

Protein arginine N-methyltransferase 1 [Source:Uniprot/ SWISSPROTAcc:Q99873]

-/- 3.90 0.54 0.62

ADP-ribosylation factor 5 [Source:RefSeq_peptideAcc:NP_001653] small GTPase mediated signal transduction/GTP binding 3.35 0.55 0.61 Argininosuccinate synthase [Source:Uniprot/SWISSPROTAcc:

P00966]

arginine biosynthetic process/argininosuccinate synthase activity, ATP binding

3.76 0.55 0.60 Serine/threonine-protein kinase 19. [Source:Uniprot/

SWISSPROTAcc:P49842]

-/- 2.25 0.57 0.54

No match -/- 3.82 0.59 0.53

Phosphate-regulating neutral endopeptidase [Source:Uniprot/ SWISSPROTAcc:P78562]

proteolysis/neprilysin activity; metallopeptidase activity, hydrolase activity, Zn ion binding

5.42 0.61 0.39

No match -/- 2.33 0.61 0.44

Kinetochore-associated protein [Source:Uniprot/SWISSPROTAcc: Q96IY1]

-/ 3.20 0.63 0.56

glyoxalase domain containing 5 [Source:RefSeq_peptideAcc: NP_001073958]

-/- 3.81 0.64 0.58

BolA-like protein 2. [Source:Uniprot/SWISSPROTAcc:Q9H3K6] -/- 1.53 0.66 0.59 Cyclin-dependent kinases regulatory subunit 1 [Source:IPIAcc:

IPI00015104]

cell cycle/cyclin-dependent protein kinase regulator activity, kinase activity

3.45 0.66 0.52 Signal peptide, CUB and EGF-like domain-containing protein 2

precursor [Source:Uniprot/SWISSPROTAcc:Q9NQ36]

-/Ca ion binding 0.09 7.02 5.19

No match -/- 0.05 5.03 2.48

Inter-alpha globulIn InhIbItor H3 [Source:IPIAcc:IPI00028413] hyaluronan metabolic process/serine-type endopeptidase inhibitor activity

0.18 4.38 2.71 Tenascin precursor [Source:Uniprot/SWISSPROTAcc:P24821] signal transduction/receptor binding 0.19 3.87 1.55 Prolargin precursor [Source:Uniprot/SWISSPROTAcc:P51888] -/- 0.16 3.06 2.05

No match -/- 0.29 3.00 1.82

No match -/- 0.54 2.76 2.82

No match -/- 0.29 2.69 2.87

No match -/- 0.29 2.62 1.75

Thrombospondin 1 precursor [Source:RefSeq_peptideAcc: NP_003237]

cell adhesion/structural molecule activity, Ca ion binding 0.29 2.53 2.28

No match -/- 0.34 2.14 1.74

No match -/- 0.51 1.81 2.02

Genes were selected according to their patterns of expression (1st

group: FCM> 1.5, FCMV< 0.67 and FCPV< 0.67; 2ndgroup: FCM< 0.67, FCMV> 1.5 and FCPV>

1.5). GO classification was subdivided in biological processes (BP) and molecular function (MF). Two-class SAM test were performed with FDR and FC limits lower than 5% and higher than 1.5.

Figure 6 Real-time PCR analysis of genes detected in VSa13 (A) and VSa16 (B) cells selected for validation. Genes were selected according to the following criteria: 2 genes with 1.5-2 FC, 2 genes with 2-10 FC, and 2 genes with FC higher than 10. All changes in gene expression evaluated by qPCR were significant according to Student’s test (p < 0.05) unless otherwise stated (non-significant - #). Genes selected for analysis were: i) secreted phosphoprotein 1 (SPP1; osteopontin), photoreceptor outer segment all-trans retinol dehydrogenase (short-chain dehydrogenase reductase; SDR), BMP-2, tissue non-specific alkaline phosphatase (TNAP), cell division protein kinase 10 (CDPK-10), S100 (calcium-binding), periostin precursor (PER), retinoic acid receptor_{b (RAR-b), suppressor of cytokine signaling 3 (SOCS-3), MGP, plasma retinol-binding protein} precursor (PRBP) and betaine homocysteine methyltransferase (BHMT) in VSa13 cells; ii) SPP1, SDR, Ras GTPase-activating-like protein (RGAP), TNAP, stathmin 1/oncoprotein 18 (STMN-1), S100, SOCS-3, Ras-related protein 1A (Rap-1A), PER, MGP, PRBP and BHMT in VSa16 cells.

bone-derived cell lines. Global analysis of gene

expres-sion of ATDC5 cells - mouse pre-chondrocytes

similar to VSa13 cells - and MC3T3-E1 cells - mouse

pre-osteoblasts similar to VSa16 cells - identified

mineralogenic genes associated with catalysis, signal

transduction, transport, transcription, structure and

motor activity [30] and metabolism, cell cycle,

signal-ing, extracellular matrix, immune response and

tran-scription [31-33], respectively. Similarity in patterns of

gene expression of mammalian and fish

pre-chondro-cyte and pre-osteoblast cell lines, suggested that

mechanisms of tissue mineralization might be

con-served among vertebrates but also among

mineralo-genic cell types.

Anti-mineralogenic activity of vanadate as a way to

identify key/novel genes involved in mineralization

Although most of the genes identified in the first step of

this analysis certainly play a role during in vitro

minera-lization, some of them must be more important than

others. These key genes were identified from the initial

bulk of genes by using the anti-mineralogenic activity of

vanadate [22,23,34]. Indeed, those genes which

expres-sion levels were oppositely regulated during in vitro

mineralization and upon treatment with vanadate were

considered as good candidates. Vanadate stimulates

pro-liferation of VSa13 cells and strongly inhibits its ECM

mineralization, and these processes seem to involve

MAPK and putative PI-3K\Ras\ERK pathways [22,23].

Genes differentially expressed under these conditions

could represent new candidate genes crucial for bone

formation. Moreover, vanadium compounds have long

been known for their insulin-like properties [35] and

role in bone formation [34,36] but to the best of our

knowledge, their effects on gene expression have never

been investigated, in particular in relation to bone.

DAVID functional annotation tool for KEGG pathways

identified genes in vanadate-treated VSa13 cells

asso-ciated with insulin signaling pathway:

3-phosphoinosi-tide-dependent protein kinase-1 in proliferating cells

and Ras homolog gene family (member Q) in

differen-tiating cells. The involvement of signaling pathways

related to insulin activity is consistent with

insulin-mimetic properties of vanadium compounds [35] and

recent studies showing that mechanisms of action of

vanadate and insulin are similar in fish VSa13 cells and

that both molecules exhibit an anti-mineralogenic

activ-ity [23]. Among the genes oppositely regulated during in

vitro

mineralization and upon treatment with vanadate

(see Table 3), two have been associated to extracellular

matrix and previously shown to play an important role

in ECM structure: tenascin (normally expressed in

mesenchymal stem cells and osteoblasts) and

thrombos-pondin (normally expressed in mesenchymal stem cells

and chondrocytes). Both have been associated to

frac-ture healing, spinal curvafrac-ture and craniofacial defects in

knock-out mice [37]. Rap1b, intermediate of MAPK

(among other pathways), ADP-ribosylation factor 5,

GTP-binding and effector of phospholipase D signaling,

and cyclin-dependent kinases regulatory subunit 1, a Ras

effector protein, were also among the genes listed in

Table 3. Identification of MAPK and Ras intermediate

genes further demonstrates the strong involvement of

MAPK pathway in the ECM mineralization of

bone-derived cells, as recently demonstrated in VSa13 [23]

and ATDC5 [38] pre-chondrocyte cells. A signal peptide

CUB and EGF-like protein

(SCUBE-like) gene was of

particular interest since SCUBE family members have

been associated with HH signaling [39], a key pathway

vanadate-treated VSa13 cells during mineralization (Mvs MV) and proliferation (Pvs PV) selected for validation. Genes were selected according to the following criteria: 3 up-regulated genes with 1.5-8 FC and 3 down-regulated genes with 1.5-8 FC. All changes in gene expression evaluated by qPCR were significant according to Student’s test (p < 0.05). Pearson correlation coefficients, calculated comparing probes 1 and 2 from oligo-array, and comparing probes 1 and 2 from oligo-array with qPCR, are indicated by r. Genes selected for analysis were: ependymin-related protein (ERP), arginine N-methyltransferase (RNM), cyclin-dependent kinase (CDK), signal peptide CUB and EGF-like protein (SCUBE-like), tenascin (TNS) and thrombospondin (TBSD).

in bone formation [1], and were recently shown to

mod-ulate/antagonize bone morphogenetic protein activity in

transgenic mouse and zebrafish [40,41]. Our data

showed an opposite regulation of SCUBE-like and

BMP-2

(associated with bone formation [21,42]) gene

expres-sion in mineralizing and vanadate-treated cells,

suggest-ing that SCUBE-like protein may play a key role in

anti-mineralogenic activity of vanadate through its action on

BMP-2 gene and/or protein. Further studies should

however be carried out in order to confirm this

hypoth-esis. Notably, numerous genes detected in this study

were classified as unknown. Absence of orthologs in

other vertebrate species, high divergence of fish genes

and/or low level of annotation in fish sequence

data-bases are likely to contribute to explain this situation. In

addition, the fact that numerous genes identified

throughout this work have not been previously linked to

bone formation, suggests that genetic mechanisms

involved in ECM mineralization and bone formation,

whether in mammalian or fish species, are still poorly

understood.

Conclusions

Global gene expression has been analyzed during ECM

mineralization of gilthead seabream vertebra-derived cell

lines using a recently developed oligo-array. A

consider-ably high number of differentially expressed genes was

detected, and occurrence of GO categories was found to

be similar in both cell lines, with approximately half of

the genes common to both cell lines. When comparing

occurrence of GO categories in VSa13 and VSa16 with

those found in mammalian systems, the similarities

found suggested conservation in

mineralization-asso-ciated processes across vertebrates. Interestingly,

enrich-ment for bone-related genes was observed in VSa16, but

not in VSa13, thus reinforcing the previously described

association of this cell line to osteoblast lineage.

Furthermore, analysis of genes differentially expressed

upon exposure to vanadate, a known anti-mineralogenic

molecule, has permitted the identification of key/novel

mineralogenic genes, which could be classified as: i)

annotated genes with known roles in bone formation (e.

g. tenascin and thrombospondin), ii) annotated genes

with unknown roles in bone formation (e.g. SCUBE-2)

and iii) unknown transcripts. Although further analyses

are required for genes included in the last two

cate-gories, the large number of transcripts detected in this

study should bring new insights into the process of

mineralization.

Methods

Cell culture and ECM mineralization

VSa13 and VSa16 cells were cultured in Dulbecco

’s

modified Eagle medium (DMEM) supplemented with

10% fetal bovine serum, 2 mM L-glutamine, antibiotics

and antimycotics (all from Invitrogen), as described

pre-viously [19]. ECM mineralization was induced in

conflu-ent cultures by supplemconflu-enting medium with 50

μg/ml

of L-ascorbic acid, 10 mM of

b-glycerophosphate and 4

mM of calcium chloride (all from Sigma-Aldrich).

Cul-ture medium was renewed every 3.5 days. At

appropri-ate times, mineral deposition was evaluappropri-ated through von

Kossa

’s staining and densitometry analysis [43].

Preparation of vanadate solution

Vanadate stock solution (5 mM, pH 6.7) was prepared

from ammonium metavanadate (Sigma-Aldrich) and

stored at 4°C.

RNA extraction and purification

Total RNA was extracted from cell cultures as described

by Chomczynski and Sacchi [44]. RNA samples were

purified using QIAGEN RNeasy Mini kit then treated

with QIAGEN RNase-free DNase according to

manufac-turer

’s instructions. RNA concentration was determined

by spectrophotometry (NanoDrop ND-1000, Thermo

Scientific) and RNA integrity evaluated by

electrophor-esis (2100 Bioanalyzer, Agilent Technologies). RNA

integrity number (RIN) index was calculated for each

sample using Agilent 2100 Expert software. Only RNA

samples with a RIN >8 were further processed.

RNA amplification, labeling and array hybridization

Total RNA (500 ng) was supplemented with a mixture

of 10 different viral polyadenylated RNAs (Agilent

Spike-In mix) then linearly amplified and labeled with

Cy3-dCTP using Agilent One-Color Microarray-Based

Gene Expression Analysis protocol. Labeled cRNA was

purified using QIAGEN RNeasy Mini kit, and sample

concentration and specific activity (pmol Cy3/

μg cRNA)

were measured by spectrophotometry. Labeled cRNA

(1650 ng) was fragmented by adding 11

μl of 10X

block-ing agent and 2.2

μl of 25X fragmentation buffer,

heat-ing at 60°C for 30 min, and finally addheat-ing 55

μl of 2X

GE hybridization buffer. Hybridization solution (100

μl)

was placed in the gasket slide and assembled to the

microarray slide (each slide containing four arrays).

Slides were incubated for 17 h at 65°C in an Agilent

hybridization oven, then dissociated from the

hybridiza-tion chamber and quickly submerged in GE wash buffer

#1 for 1 min. An additional wash was performed in

pre-warmed (37°C) GE wash buffer #2 for another 1 min.

Slides were scanned using Agilent G2565BA DNA

microarray scanner. Scan resolution was set to 5

μm

and two different sensitivity levels (XDR Hi 100% and

XDR Lo 10%) were used. Both images were analyzed

simultaneously using the standard procedures described

in Agilent Feature Extraction software 9.5.1.

Data normalization and statistical analysis

Spots with unsuitable integrity and intensity were

fil-tered out using Feature Extraction Software 9.5.1 flag

“glsFound”. Flag value is set to 1 if the spot has an

intensity value significantly different from the local

background, 0 otherwise. Spike-in control intensities

(Spike-In Viral RNAs) were used to identify the best

normalization procedure for each dataset. After

normali-zation, spike intensities are expected to be uniform

across the experiments of a given dataset. Quantile or

cyclic Lowess normalization was performed using R

sta-tistical software (available at http://www.r-project.org)

then SAM statistical test [45] was used to identify

differ-entially expressed genes between groups.

Analysis of gene expression by quantitative real-time PCR

QPCR was performed using iCycler iQ system

(Bio-Rad). Total RNA (1

μg) was treated with RQ1

RNase-free DNase (Promega) then reverse-transcribed at 37°C

according to manufacturer

’s instruction using Moloney

murine leukemia virus reverse transcriptase and

univer-sal oligo-dT adapter (5

’-ACGCGTCGACCTCGAGATC-GATG(T)13-3’). PCR amplification of cDNA fragments

was performed using the iQ SYBR Green I mix, specific

primers and 10 ng of reverse-transcribed RNA. The

fol-lowing PCR conditions were used: an initial

denatura-tion step at 95°C for 4 min then 40-50 cycles of

amplification (each cycle is 30 s at 95°C, 30 s at 68°C).

Fluorescence was measured at the end of each extension

cycle in the FAM-490 channel. Levels of gene expression

were calculated using the

ΔΔCt method and normalized

using expression levels of ribosomal protein L27a

(RPL27a) housekeeping gene.

Additional material

Additional file 1: Biological processes GO entries occurrence among common differentially expressed genes in controlversus

mineralized VSa13 and VSa16 cells. Raw data was normalized using quantile method. A two class SAM test was performed; FDR and FC parameters were lower than 5 and higher than 1.5, respectively. Additional file 2: Molecular function GO entries occurrence among common differentially expressed genes in controlversus

mineralized VSa13 and VSa16 cells. Raw data was normalized using quantile method. A two class SAM test was performed; FDR and FC parameters were lower than 5 and higher than 1.5, respectively. Additional file 3: Cellular component GO entries occurrence among common differentially expressed genes in control_versus

mineralized VSa13 and VSa16 cells. Raw data was normalized using quantile method. A two class SAM test was performed; FDR and FC parameters were lower than 5 and higher than 1.5, respectively. Additional file 4: Gene description (according to SAPD database [28]), GO classification and FC of up-regulated genes in VSa13 cells with FC higher than 10 in controlversus mineralization. GO classification was subdivided in biological processes (BP), molecular function (MF) and cellular component (CC). Raw data was normalized using quantile method and then a two class SAM test was performed; FDR was limited to 5%.

Additional file 5: Gene description (according to SAPD database [28]), GO classification and FC of up-regulated genes in VSa16 cells with FC higher than 10 in controlversus mineralization. GO classification was subdivided in biological processes (BP), molecular function (MF) and cellular component (CC). Raw data was normalized using quantile method and then a two class SAM test was performed; FDR was limited to 5%.

Additional file 6: Gene description (according to SAPD database [28]), GO classification and FC of down-regulated genes in VSa13 cells with FC higher than 10 in controlversus mineralization. GO classification was subdivided in biological processes (BP), molecular function (MF) and cellular component (CC). Raw data was normalized using quantile method and then a two class SAM test was performed; FDR was limited to 5%.

Additional file 7: Gene description (according to SAPD database [28]), GO classification and FC of down-regulated genes in VSa16 cells with FC higher than 10 in controlversus mineralization. GO classification was subdivided in biological processes (BP), molecular function (MF) and cellular component (CC). Raw data was normalized using quantile method and then a two class SAM test was performed; FDR was limited to 5%.

List of abbreviations

BMP-2: bone morphogenetic protein 2; DAVID: database for annotation, visualization and integrated discovery; ECM: extracellular matrix; EST: expressed sequence tag; FC: fold change; FDR: false discovery rate; GO: gene ontology; MGP: matrix gla protein; OC: osteocalcin; qPCR: quantitative real-time PCR; SAM: significance analysis of microarray; SCUBE-like: signal peptide, CUB and EGF-like protein; SPP1: secreted phosphoprotein 1; osteopontin Acknowledgements

This work was partially funded by FP6 EU grant n° 505403 (Marine Genomics Europe NoE). DMT was supported by a postdoctoral fellowship (SFRH/BPD/ 45034/2008) from the Portuguese Foundation for Science and Technology (FCT). Author details

1

Centre of Marine Sciences (CCMAR), University of Algarve, Faro, Portugal.

2_{Department of Public Health, Comparative Pathology and Veterinary}

Hygiene, University of Padova, Legnaro, Italy.3CRIBI, University of Padova, Complesso Biologico Vallisneri, Via Ugo Bassi 58/B, Padova, Italy.

4

Department of Biomedical Sciences and Medicine, University of Algarve, Faro, Portugal.

Authors’ contributions

DMT performed the cell culture experiments, carried out RNA sample preparation and qPCR experiments, participated in oligo-array hybridization, data extraction and data interpretation, and drafted the manuscript. VL participated in the study design and coordination, and helped to draft the manuscript. LB participated in data interpretation and coordination of the study. SF carried out oligo-array hybridization and data extraction, and participated in data interpretation. CR performed the statistical analysis. MLC conceived the study, participated in its design and coordination and contributed to the final draft of the manuscript.

All authors read and approved the final version of the manuscript. Received: 29 November 2010 Accepted: 13 June 2011 Published: 13 June 2011

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doi:10.1186/1471-2164-12-310

Cite this article as: Tiago et al.: Global analysis of gene expression in mineralizing fish vertebra-derived cell lines: new insights into anti-mineralogenic effect of vanadate. BMC Genomics 2011 12:310.